PDF电子书：Yeast Systems Biology

zsg

/ ?1 ?( I+ j8 E$ t. w7 L; {1 H  p+ X# a: n
Yeast Systems Biology
3 ~2 r7 h5 S7 ]9 s2 c- WMethods and Protocols6 T5 U- R2 _2 Y
Edited by
7 q4 Q: g( K2 [  YJuan I. Castrillo
/ ?6 B  ?) t% G5 ~4 b
4 c2 q0 i, A1 yContents
4 v1 m" b1 z7 i2 E( QPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v% Y/ q- F9 e' F, V, h+ x
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
7 ^: Q7 X) y) {$ l4 `SECTION I: YEAST SYSTEMS BIOLOGY
# h3 r- B* R" l+ m+ o9 }- U& Y1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3$ M( ~7 s, i; k/ N; V5 ?
Juan I. Castrillo and Stephen G. Oliver
+ M& s7 j3 `& F5 T0 ~) oSECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE
& `# @4 M' {$ Q, IAND MOLECULAR STUDIES3 W' q" t' ]  h
2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State
$ u1 c& f7 B/ F) ^& r( wof the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31
4 h  m! w$ i3 D8 P4 w, N$ u- r4 NEd Louis! B, r( z" O* N3 }( Y2 k6 }" d
3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 41
0 |+ \! ?0 \2 d/ M) [9 K: aOliver J. Rando5 v8 I9 P: f0 f6 s, a! N, n% f& ^! ~5 ?
4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 61
7 J. X( S2 r7 O8 |+ e5 ]# K3 yDustin E. Schones, Kairong Cui, and Suresh Cuddapah, k  s/ L* w4 N4 G
5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 739 S% _" [3 ]0 z3 |, Z4 D
Andrew Hayes, Bharat M. Rash, and Leo A.H. Zeef
& K8 N4 ^0 x% l2 F7 J" N0 I6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide0 R) ]* H6 r9 v# i  A8 Z+ Y( V3 N
Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
0 [& ?) X/ k- e  c) q) R! uHelen Neil and Alain Jacquier
1 d  Q2 [3 Z' f/ v$ X% F7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density
3 h; w) X/ y) X! J6 JTiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1079 l# j$ ^, U4 j; @! m% @
Lior David, Sandra Clauder-Münster, and Lars M. Steinmetz' O% n: ]3 Z7 B
8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
# }0 W5 I( W; v2 d/ NKarl Waern, Ugrappa Nagalakshmi, and Michael Snyder& o) B7 K1 j' @6 J- B: G: K
9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133
; J6 L$ g$ x9 p3 n7 {6 r* @" V$ HTraude H. Beilharz and Thomas Preiss; H7 Z$ m5 U& U/ h6 r- c8 i
10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 1497 }/ h9 Y, S3 `
Johanna Rees and Kathryn Lilley1 s+ S/ H' \8 D5 b+ r  L9 F0 N3 c
11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 179
( y5 `0 T6 K; r  M; d/ iAmy J. Claydon and Robert J. Beynon
' c# z7 o+ D7 O/ x12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197* o2 g% |4 J/ n, g; J, m
Benoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick,
: k4 w& v7 ^) QDavid E. Hill, and Marc Vidal% x* m* L' R; n9 R& H% }' T: c) }
13. Use of Proteome Arrays to Globally Identify Substrates for E33 K1 P" O' k; |' p# d' c
Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
+ z; i( E& p8 ?Avinash Persaud and Daniela Rotin
. I  O3 v, t; R3 i14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic
; B) O  r0 ~1 C2 P3 J! T# m, iProfiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 2259 T! y2 q5 `" j. j/ P  t4 @
Catherine L. Winder and Warwick B. Dunn
2 V7 g6 e: ?8 s0 V1 \' x, R7 {2 c15. The Automated Cell: Compound and Environment Screening System$ e# ^2 J( p/ D% M+ [
(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 239! l: A9 w! v# L1 g( H6 a" Y
Michael Proctor, Malene L. Urbanus, Eula L. Fung,0 S+ M& m, g; |* @2 B; ~' G2 O
Daniel F. Jaramillo, Ronald W. Davis, Corey Nislow,: k) U6 O. U  k; D0 p
and Guri Giaever! k5 {6 U6 G# \( U! t3 N  O
16. Competition Experiments Coupled with High-Throughput Analyses for
4 A* J" P8 }+ C1 I- k; U- {Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271
' W* j: ~: L; D9 g5 f7 r, ]; dDaniela Delneri
+ L" h' `2 f6 {17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for8 F$ A3 X( L( P9 S& b8 C4 x
Examination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283: X1 v9 E- B4 L1 A4 E
Brian D. Slaughter, Jay R. Unruh, and Rong Li" G: P7 f/ J) j. T# S
18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307
) d1 m  c8 V: J1 q+ `0 t1 f, YYuehua Wei and X.F. Steven Zheng1 r. M+ X  C0 B3 d8 W
SECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES6 e" }. B. s& ^3 Z  j$ C8 a: ?
AND ANALYSES5 G8 g" C2 U  P
19. Computational Yeast Systems Biology: A Case Study for the MAP
6 t" e3 ]' Q/ q9 AKinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
5 H4 i- q6 \: v- h; S/ l" b8 C4 _Edda Klipp1 T' H, G% F7 t* ^
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345
8 m: f( n1 q  ~8 SAxel Kowald and Christoph Wierling
: c+ \  X  |: i21. A Computational Method to Search for DNA Structural Motifs in
/ [- D. l3 X8 z8 t) \( EFunctional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 367
9 W% X# R2 Y3 O! V. [  YStephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius
- [9 J% K. W3 k/ {3 E1 }22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 3814 c9 ~3 v( t9 ^+ Y0 }
Shoshana J. Wodak, Jim Vlasblom, and Shuye Pu
  ^! @  H7 P- _6 S# z% L( F5 a6 r23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407/ T1 Q/ ^& h8 D  t4 _" J/ ~
Yitzhak Pilpel
/ r2 g4 e% T9 j1 V( g5 O5 K24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic
$ D1 N0 G0 d# k0 {! W  l1 J' PProcesses in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427; g+ _" Y0 X! s4 U" `4 h; c
Jean-Marc Schwartz and Claire Gaugain' k5 A* x0 c+ c+ [1 Q
25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 4457 r6 {! K$ O5 M
Intawat Nookaew, Roberto Olivares-Hernández, Sakarindr
4 w& `' i- z0 Y0 Z7 ~, MBhumiratana, and Jens Nielsen
6 }/ ~1 J- t4 s# i26. Representation, Simulation, and Hypothesis Generation in Graph
  N0 d1 Y; ?2 k' d, S2 U  r) Uand Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 4659 {& U$ C: v" u; Q1 y) I: d7 R0 i
Ken Whelan, Oliver Ray, and Ross D. King
( `; n) g& A' j% K9 ^27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 483
: N$ z: |; B- G/ R2 r6 j  EBalázs Papp, Balázs Szappanos, and Richard A. Notebaart
: U" ]( C6 {. n+ [SECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE, z5 J- h& |0 ^5 x0 g
AS A TOOL FOR MAMMALIAN STUDIES
# U# @4 ?$ O; D5 ?28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian
! ~# Q0 x! G9 w) F# HGene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501. X5 ~* Y7 R' @9 P2 j
Nianshu Zhang and Elizabeth Bilsland
2 q- y0 k5 N9 F; |: ]- {) hSubject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525+ b+ w/ s" P6 T* p; E  ?
[hide][/hide]

yunshu

(*^__^*) 嘻嘻……   

aliyun

谁能送我几分啊  

三好学生

免疫细胞疗法治疗肿瘤有效  

舒思

原来这样也可以  

xm19

给我一个女人，我可以创造一个民族；给我一瓶酒，我可以带领他们征服全世界 。。。。。。。。。  

htc728

老大,我好崇拜你哟  

干细胞2014

真好。。。。。。。。。  

365wy

嘿嘿......哈哈......呵呵.....哟~呼  

xiao2014

不错!